1 code implementation • 17 Jun 2021 • Tom Blau, Gilad Francis, Philippe Morere
To address this shortcoming, we propose Probabilistic Planning for Demonstration Discovery (P2D2), a technique for automatically discovering demonstrations without access to an expert.
no code implementations • 21 Apr 2020 • Arjun Prakash, Nick James, Max Menzies, Gilad Francis
We develop a new method to find the number of volatility regimes in a nonstationary financial time series by applying unsupervised learning to its volatility structure.
no code implementations • 20 Jan 2020 • Philippe Morere, Gilad Francis, Tom Blau, Fabio Ramos
Balancing exploration and exploitation remains a key challenge in reinforcement learning (RL).
no code implementations • 25 Sep 2019 • Weiming Zhi, Tin Lai, Lionel Ott, Gilad Francis, Fabio Ramos
This generally involves the prediction and understanding of motion patterns of dynamic entities, such as vehicles and people, in the surroundings.
no code implementations • 8 Sep 2019 • Tin Lai, Philippe Morere, Fabio Ramos, Gilad Francis
In this work, we introduce a local sampling-based motion planner with a Bayesian learning scheme for modelling an adaptive sampling proposal distribution.